easystats

How to summarize the total effect of a categorical variable like education? A new vignette shows how to compute absolute and relative inequality with the #easystats {modelbased}📦in #rstats. Get a single, interpretable number to quantify overall group disparities!
easystats.github.io/modelbased/a...

Modelbased for Quick and Beautiful Model Visualization in #rstats imachordata.com/2025/07/25/m... Thanks, @easystats.github.io!

Just dodging is not yet implemented in {tinyplot}, but hopefully coming soon!

As you can see, many plot types already work, just some fine-tuning left to do...

🎉 Great news, R users! 🎉 We're thrilled to announce that {tinyplot} support is coming to the #rstats #easystats project! Get ready for even more amazing stuff to make your data analysis a breeze! 📊✨
@gmcd.bsky.social @vincentab.bsky.social @zeileis.org

Since `display(format = "tt")` returns a `tinytable` object, you can easily modify the table to meet your needs.

Here's the default HTML rendering.

Improved support for the great {tinytable}📦 from @vincentab.bsky.social coming to the easystats packages! Use the `display()` method for different output formats of your tables - HTML, markdown, or - when `format = "tt"` a `tinytable` object that renders context-dependent.
#easystats #rstats

#statstab #386 {bayestestR} Evaluating Evidence and Making Decisions using Bayesian Statistics by @mattansb.msbstats.info

Thoughts: Want to start using Bayesian stats? Here is a quick but comprehensive guide in #R

#bayesian #bayes #mcmc #easystats #guide

mattansb.github.io/bayesian-evi...

screenshot showing the row dataframe with a column of rvars and the markdown-formatted-table output of describe_posterior() + print_md()

bayestestR::describe_posterior() works on rvar columns

But I think the rvar-support is more recent ;-)

You may find some of the resources useful, e.g. there are two sets of slides for the {modelbased} package: easystats.github.io/easystats/ar...
(and of course the website, which gets regular updates: easystats.github.io/modelbased/)

- 📦parameters, performance: improvement to all functions related to factor or principal component analysis, as well as psychometric testing (Cronbach's alpha, omega...)
- 📦modelbased: better support for brms-mixture models

More updates in other packages are in the pipeline...

Several easystats📦were updated the past weeks, make sure to install them to get the latest features!

Here's what's new:
- 📦insight, bayestestR: performance improvements for Bayesian models, better support for brms-mixture models

1/2

#easystats #rstats
easystats.github.io/easystats/

Yay, we have reached the 30 million downloads mark (and > 10k citations of our packages)! #easystats #rstats
(nice metrics, despite not 100% accurate, but still...)

Since we got questions regarding if model predictors also predict class membership or only the mean outcome for each class, we added a short paragraph including a summary table and some example code at the end of the vignette, clarifying the different GMM options:
easystats.github.io/modelbased/a...

Note that these new features require the current GitHub versions of our packages, which you can install via `easystats::install_latest()`.

Unlock hidden patterns in longitudinal data! 🚀 Our new vignette shows how to use brms & easystats to perform Growth Mixture Models, identify unique developmental trajectories, and visualize & interpret your findings with ease. #rstats #brms #easystats
easystats.github.io/modelbased/a...

We do use the same approach as car::vif() (we actually report gvif).

If you have an example where our output deviates from car::vif() please do share!

Personally I prefer using datawizard::standardize(), and specifically using it *in the formula*.

So
mtcars$hp_z <- scale(mtcars$hp)
mpg ~ hp_z

Becomes
mpg ~ standardize(hp)

This solves both issues you raise in your post.

#rstats @easystats.bsky.social

And there's a non-documented option "specific", which is equivalent to `estimate_relation()`.

Page 3 in the paper (or the docs: easystats.github.io/modelbased/r...)

Yes, see argument `estimate`. Using this argument, you should be able to easily reproduce results from emmeans and marginaleffects::avg_predictions. It's more a matter of naming things/wording, where the modelbased approach differs from emmeans or marginaleffects.

We're happy to have an accompanying publication for another #rstats #easystats package published! Thanks to @vincentab.bsky.social and @tjmahr.com for reviewing the manuscript!

Messy data is our bread and butter!

Yes, exactly 💯

performance::check_group_variation(iris, select = "Sepal.Width", by = "Species") #> Check Species variation #> #> Variable | Variation | Design #> -------------------------------- #> Sepal.Width | both | datawizard::demean(iris, select = "Sepal.Width", by = "Species") |> dplyr::glimpse() #> Rows: 150 #> Columns: 7 #> $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.… #> $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.… #> $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.… #> $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.… #> $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s… #> $ Sepal.Width_between <dbl> 3.428, 3.428, 3.428, 3.428, 3.428, 3.428, 3.428, 3.428, 3… #> $ Sepal.Width_within <dbl> 0.072, -0.428, -0.228, -0.328, 0.172, 0.472, -0.028, -0.0…

This function can also be used to detect any predictors that might cause heterogeneity bias - variable that vary both within and between groups, that can be treated with datawizard::demean()

easystats.github.io/datawizard/r...

mlmRev::egsingle |> performance::check_group_variation( select = c("female", "grade", "math"), by = c("schoolid", "childid"), include_by = TRUE ) #> Check schoolid variation #> #> Variable | Variation | Design #> ------------------------------ #> childid | both | nested #> female | within | crossed #> grade | both | #> math | both | #> #> Check childid variation #> #> Variable | Variation | Design #> ----------------------------- #> schoolid | between | #> female | between | #> grade | both | #> math | both |

🆕 Introducing check_group_variation() in the {performance} #Rstats package! 🎉

This function makes it easy to checks if variables vary within or between levels of grouping variables.

Perfect for understanding and designing mixed models 🚀

easystats.github.io/performance/...

#stats #easystats

It's slightly different, it adjusts for trial count.

One function per week, this time we look closer at random effects variances in mixed models: `performance_reliability()` & `performance_dvour()`. Is the variability in your data due to noise within groups, or actual differences between groups? #easystats #rstats easystats.github.io/performance/...